首页 >  2022, Vol. 26, Issue (1) : 231-242

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全文摘要次数: 354 全文下载次数: 420
引用本文:

DOI:

10.11834/jrs.20221279

收稿日期:

2021-05-18

修改日期:

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2002年—2018年太湖水体溶解二氧化碳浓度卫星遥感数据集
齐天赐1,2,段洪涛1,3,曹志刚1,沈明1,2,肖启涛1,刘东1,马金戈1,2
1.中国科学院南京地理与湖泊研究所 中国科学院流域地理学重点实验室, 南京 210008;2.中国科学院大学, 北京 100049;3.西北大学 城市与环境学院, 西安 710027
摘要:

湖泊在全球碳循环中发挥着重要作用,而湖泊中溶解CO2浓度(cCO2)控制着其CO2通量的方向及大小,是湖泊CO2排放估算的关键参数。中国第三大淡水湖——太湖虽然具有长期的野外监测数据,但其样点分布在空间和时间上并不均匀,很可能给其CO2排放的估算带来不确定性和偏差。有必要利用更高频率和覆盖范围地遥感手段来弥补野外监测在时空代表性上的不足。本文基于MODIS/Aqua数据反演的叶绿素a浓度、表层水温、漫衰减系数及光合有效辐射产品,通过二次多项式经验模型对太湖藻型湖区表层水体cCO2进行逐像元的估算。并对结果进行数据质量控制和统计平均得到2002-07—2018-12长时序月平均cCO2数据集。数据集由GeoTiff格式储存,使用GCS_WGS_1984地理坐标系,共包含198个文件。产品估算精度验证结果显示,星地同步像元-样点的遥感估算结果与野外数据的偏差在总体上较小(均方根误差RMSE=12.83 μmol·L-1,无偏百分比偏差UPD=24.03%)。同时遥感与野外数据估算的年均值在太湖各个湖区表现出很好的一致性(RMSE<13.24 μmol·L-1,UPD<25.82%),证明数据的可信度。产品的不确定性评估结果显示,在所有输入变量的随机误差影响下,月均cCO2产品最大可能高估约30%。基于本数据集数据统计得到的结果显示,太湖cCO2表现出明显的季节变化,冬春高夏秋低,西部高东部低;且太湖年平均cCO2在数据集覆盖时间段内表现出显著下降趋势(0.80 μmol·L-1·a-1)。本数据集(下载方式:https://doi.org/10.5281/zenodo.4729048[2021-05-18])月平均时间尺度同常规生态环境监测对应,便于分析对比,并且提供了空间分异信息,能够辅助研究深入理解太湖cCO2乃至碳循环过程的时空变化规律,值得推广使用。

Monthly average satellite-estimated dataset of Lake Taihu’s dissolved carbon dioxide concentration from 2002 to 2018
Abstract:

Lakes play an important role in the global carbon cycle. The dissolved carbon dioxide concentration (cCO2) controls the direction and amount of the lake CO2 flux, which makes it one of the keys to the Lake CO2 emission estimates. Due to the limitations of traditional field surveys on the spatiotemporal representativeness, large efforts of field surveys are still required to fulfil the requirements of monitoring lake cCO2 dynamics. China’s third largest freshwater lake—Lake Taihu is a hot spot for lake carbon cycle and eutrophication research because of its complex environmental problems. Although Lake Taihu has long-term field limnological observations, including the measurements of physical, chemical, and biological parameters, the spatiotemporal distributions of sampling sites are still limited for the accurate estimation of the CO2 emissions, which is likely to give uncertainty and deviation to its CO2 emission estimates. It is necessary to take advantages of high frequency and wide range remote sensing technologies for achieving larger-scale and longer-term estimations of lake cCO2 dynamics compared to field surveys.In this paper, we used the MODIS-derived chlorophyll-a concentration, lake surface temperature, diffuse attenuation coefficient of photosynthetically active radiation, and photosynthetically active radiation to estimate daily cCO2 of Lake Taihu (the coefficient of determination R2=0.84, root mean square error RMSE=11.81 μmol·L-1, unbiased percent difference UPD=22.46%). After data quality control, the daily cCO2 were averaged on a monthly scale to obtain the monthly average cCO2 of Lake Taihu. The data was stored in GeoTIFF grid format, with the GCS_WGS_1984 geographic coordinate system. The dataset contains 198 files of monthly average cCO2 of Lake Taihu from July 2002 to December 2018.The uncertainty assessment results of the product show that under the influence of all input variables, the monthly cCO2 product would overestimate about 30%. The differences between cCO2 of pixel-sample matchups were small in total (Root mean standard error RMSE=12.83 μmol·L-1, non-bias percentage deviation UPD=24.03%). The annual average of cCO2 estimated by field observation and MODIS were consistent with each other in different regions of Lake Taihu (Root mean standard error RMSE <13.24 μmol·L-1, non-bias percentage deviation UPD <25.82%). Based on the monthly average dataset, the cCO2 of Lake Taihu showed significant seasonal dynamics, which were was low in summer and autumn (June to November) and eastern region, and high in winter and spring (December to May) and western region. Besides, the annual average cCO2 showed a significant declining trend (0.80 μmol·L-1·a-1, p<0.01).This monthly average dataset (The download address is https://doi.org/10.5281/zenodo.4729048) corresponds to the time scale of traditional limnological and ecological observations, which is suitable for comparison and analysis with traditional field datasets. Besides, the satellite dataset provides more spatial details of cCO2. It is very enlightening for better understanding of the biogeochemical process associated with cCO2 in Lake Taihu. We believed this dataset would be very worth promoting to all researchers focusing on Lake Taihu.

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